To meet those economic demands, the Web IQ platform doesn’t deliver whole documents to querying agents. Whole documents can lead to expensive inference further down the chain, as LLMs process results repeatedly to drive the agent workflow. Instead, Web IQ structures the information retrieved from the underlying search engine data, delivering what Microsoft calls “structured evidence objects” as well as passage-level information from unstructured text documents. This should result in a much higher signal-to-noise ratio than simply querying a search engine, with a focus on delivering information that lets agents work using fewer tokens.
Using Web IQ in your agent code
The API for Web IQ is a standard REST call, delivering a request object to the Web IQ endpoint. Along with your API authorization key, you will send a query, a set of parameters that control the number of results returned, the language and region used, and the maximum size of the responses and the format used. Responses can be returned in text, HTML, or markdown formats, as well as extracted passages that are selected for context. All other options return the full document, so can be more expensive to use. Markdown is an interesting alternative, as it can be used as the basis for giving agents semantic memories.
Results include important contextual and citation information, including web page titles and URLs, as well as data about when the site was last crawled and how stale the underlying information is. This can be used to improve grounding and provide more information that can be included in formatted responses — much in the same way as Bing’s Copilot displays context in the form of footnotes in its responses.
